Promoting Complex Systems Learning through the use of Computer Modeling

Authors: Kamel Hashem, David Mioduser

Abstract:

This paper describes part of a project about Learningby- Modeling (LbM). Studying complex systems is increasingly important in teaching and learning many science domains. Many features of complex systems make it difficult for students to develop deep understanding. Previous research indicates that involvement with modeling scientific phenomena and complex systems can play a powerful role in science learning. Some researchers argue with this view indicating that models and modeling do not contribute to understanding complexity concepts, since these increases the cognitive load on students. This study will investigate the effect of different modes of involvement in exploring scientific phenomena using computer simulation tools, on students- mental model from the perspective of structure, behavior and function. Quantitative and qualitative methods are used to report about 121 freshmen students that engaged in participatory simulations about complex phenomena, showing emergent, self-organized and decentralized patterns. Results show that LbM plays a major role in students' concept formation about complexity concepts.

Keywords: Complexity, Educational technology, Learning by modeling, Mental models

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1074809

References:

[1] Blikstein, P., and Wilensky, U. (2005). Less is more: agent-based simulation as a powerful learning tool in materials science. Proceedings of the IV International Joint Conference on AAMAS. Utrecht, Holland.
[2] Buckley, C. B., Gobert, J., Kindfield, A., Horwitz, P., Tinker, R., Gerlits, B., et al. (2004). Model-Based Teaching and Learning with BioLogica: What do they learn? How do they learn? How do we know? Journal of Science Education and Technology , 13 (1), 23-41.
[3] Chen, D., and Stroup, W. (1993). General system theory: Toward a conceptual framework for science and technology education for all. Journal of Science Education and Technology , 2 (3), 447-459.
[4] Chi, M. T. (2005). Commonsense conceptions of emergent processes: why some misconceptions are robust? The Journal of the Learning Sciences, 14 (2), 161-199.
[5] Gilbert, K. J., and Boulter, J. C. (Eds.). (2000). Developing models in science education. Dordrecht, Holland: Kluwer Academic Publishers.
[6] Gobert, D. J., and Buckley, C. B. (2000). Introduction to model-based teaching and learning in science education. International Journal of Science Education, 22 (9), 891-894.
[7] Gobert, J. (2003). Harnessing technology to support on-line model building and peer collaboration.
[8] Hashem, K., and Mioduser, D. (2011). The Contribution of Learning by Modeling (LbM) to Students' Understanding of Complexity Concepts. International Journal of e-Education, e-Business, e-Management and e- Learning (IJEEEE , 1 (2), 151-155.
[9] Hmelo-Silver, C., and Pfeffer, M. G. (2004). Comparing expert and novice understanding of a complex system from the perspective of structures, behaviors, and functions. Cognitive Science , 28 (1), 127-138.
[10] Jacobson, M. (2001). Problem solving, cognition, and complex systems: Differences between experts and novices. Complexity , 6 (3), 41-49.
[11] Jacobson, M., and Wilensky, U. (2006). Complex systems in education: scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences , 15 (1), 11-34.
[12] Levy, S., and Wilensky, U. (2005). An analysis of student patterns of exploration with NetLogo models embedded in the connected chemistry environment. Proceedings of the annual meeting of the American Educational Research Association. Montreal, CA.
[13] Louca, L., and Constantinou, C. (2003). The use of computer-based microworlds for developing modeling skills in physical science: an example from light. International Journal of Science Education .
[14] Norman, D. A. (1983). Some observations on mental models. In D. Gentner, & A. L. Stevens (Eds.), Mental models. Hillsdale, NJ: Erlbaum.
[15] Resnick, M. (1994). Changing the centralized mind. Cambridge, MA: MIT press.
[16] Resnick, M. (1996). Beyond the centralized mindset. Journal of the Learning Sciences , 5 (1), 1-22.
[17] Resnick, M., and Wilensky, U. (1998). Diving into Complexity: Developing probabilistic decentralized thinking through role-playing activities. Journal of Learning Sciences , 7 (2), 153-172.
[18] Stieff, M., and Wilensky, U. (2003). Connected chemistry-incorporating interactive simulations into the chemistry classroom. Journal of Science Education and Technology, 12 (3), 285-302.
[19] Vattam, S. S., Goel, A. K., Rugaber, S., Hmelo-Silver, C. E., Jordan, R., Gray, S., et al. (2011). Understanding Complex Natural Systems by Articulating Structure-Behavior-Function models. Educational Technology & Society, 14 (1), 66-81.
[20] Wilensky, U., and Resnick, M. (1999). Thinking in levels: A dynamic systems approach to making sense of the world. Journal of Science Education and Technology, 8 (1), 3-19.
[21] Yehezkel, C., Ben-Ari, M., and Dreyfus, T. (2005). Computer architecture and mental models. ACM , 101-105.